Market-1501_Attribute

by vana77

27 hand-annotated attributes of Market-1501

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Market-1501_Attribute

The evaluation code will be added soon.

About dataset

We annotate 27attributes for Market-1501. The original dataset contains 751 identities for training and 750 identities for testing. The attributes are annotated in the identity level, thus the file contains 28 x 751 attributes for training and 28 x 750 attributesfor test, where the label "imageindex" denotes the identity. The annotations are contained in the file marketattribute.mat.

The 27 attributes are:

| attribute | representation in file | label | | :----: | :----: | :----: | | gender | gender | male(1), female(2) | | hair length | hair| short hair(1), long hair(2) | | sleeve length | up | long sleeve(1), short sleeve(2) | | length of lower-body clothing | down | long lower body clothing(1), short(2) | | type of lower-body clothing| clothes| dress(1), pants(2) | | wearing hat| hat | no(1), yes(2) | | carrying backpack| backpack | no(1), yes(2) | | carrying bag| bag | no(1), yes(2) | | carrying handbag| handbag | no(1), yes(2) | | age| age | young(1), teenager(2), adult(3), old(4) | | 8 color of upper-body clothing| upblack, upwhite, upred, uppurple, upyellow, upgray, upblue, upgreen | no(1), yes(2) | | 9 color of lower-body clothing| downblack, downwhite, downpink, downpurple, downyellow, downgray, downblue, downgreen,downbrown | no(1), yes(2) |

Note that the though there are 8 and 9 attributes for upper-body clothing and lower-body clothing, only one color is labeled as yes (2) for an identity.

Sample

Evaluation

To evaluate, you need to predict the attributes for test data(i.e., 13115 x 12 matrix) and save them in advance. "gallerymarket.mat" is one prediction example. Then download the code "evaluatemarket_attribute.m" in this repository, change the image path and run it to evaluate.

Citation

If you use this dataset in your research, please kindly cite our work as,

@article{lin2019improving,
  title={Improving Person Re-identification by Attribute and Identity Learning},
  author={Lin, Yutian and Zheng, Liang and Zheng, Zhedong and Wu, Yu and Hu, Zhilan and Yan, Chenggang and Yang, Yi},
  journal={Pattern Recognition},
  doi = {https://doi.org/10.1016/j.patcog.2019.06.006},
  year={2019}
}
Market-1501 Dataset:
@inproceedings{zheng2015scalable,
  title={Scalable person re-identification: A benchmark},
  author={Zheng, Liang and Shen, Liyue and Tian, Lu and Wang, Shengjin and Wang, Jingdong and Tian, Qi},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2015}
}

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